Update app.py

app.py

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+import os, numpy as np, torch, gradio as gr, librosa
+from huggingface_hub import hf_hub_download
+from model import DCCRN  # 确保你上传了 model.py 和 utils 依赖
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+SR = 16000  # 你的模型训练采样率
+
+# 从环境变量里读取模型仓库名和权重文件名
+REPO_ID  = os.getenv("MODEL_REPO_ID", "Ada312/DCCRN")   # 你的模型仓库
+FILENAME = os.getenv("MODEL_FILENAME", "dccrn.ckpt")    # 权重文件
+TOKEN    = os.getenv("HF_TOKEN")  # 如果模型仓库是私有，就需要这个
+
+# 下载权重到本地缓存
+ckpt_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, token=TOKEN)
+
+# 初始化模型并加载权重
+net = DCCRN()
+ckpt = torch.load(ckpt_path, map_location=DEVICE)
+state = ckpt.get("state_dict", ckpt)
+state = {k.replace("model.","").replace("module.",""): v for k,v in state.items()}
+net.load_state_dict(state, strict=False)
+net.to(DEVICE).eval()
+
+# 推理函数：输入 noisy audio → 输出 enhanced audio
+def enhance(audio_path: str):
+    wav, _ = librosa.load(audio_path, sr=SR, mono=True)
+    x = torch.from_numpy(wav).float().to(DEVICE)[None, None, :]
+    with torch.no_grad():
+        y = net(x).squeeze().cpu().numpy()
+    return (SR, y)
+
+# Gradio 界面
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎧 DCCRN Speech Enhancement\n上传或录音，点击“去噪”。")
+    with gr.Row():
+        inp = gr.Audio(sources=["upload","microphone"], type="filepath", label="Noisy speech")
+        out = gr.Audio(label="Enhanced speech")
+    gr.Button("去噪").click(enhance, inputs=inp, outputs=out)
+
+demo.queue(concurrency_count=1, max_size=8)
+demo.launch()